Current status and future developments in predicting outcomes in radiation oncology.
Current status and future developments in predicting outcomes in radiation oncology.
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DOI：
10.1259/bjr.20220239
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发表时间：
2022-10-01
期刊：
影响因子：
--
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文献类型：
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数据驱动技术的进步和信息丰富的多重组学特征的纳入显著提高了放射肿瘤学结果建模的性能。为了使目前的趋势可持续，与稳健数据建模相关的挑战，如小样本、低尺寸特征比、噪声数据，以及与算法建模相关的问题，如复杂性、不确定性和可解释性，如果得不到解决，则需要得到缓解。新兴的计算技术和新范式，如联合学习、人在环中、量子计算和新的可解释性方法，在克服这些挑战和弥合放射治疗中精确结果建模的差距方面显示出巨大的潜力。将介绍这些有前景的技术的例子，并讨论它们在改进结果建模方面的潜在作用。
Advancements in data-driven technologies and the inclusion of information-rich multiomics features have significantly improved the performance of outcomes modeling in radiation oncology. For this current trend to be sustainable, challenges related to robust data modeling such as small sample size, low size to feature ratio, noisy data, as well as issues related to algorithmic modeling such as complexity, uncertainty, and interpretability, need to be mitigated if not resolved. Emerging computational technologies and new paradigms such as federated learning, human-in-the-loop, quantum computing, and novel interpretability methods show great potential in overcoming these challenges and bridging the gap towards precision outcome modeling in radiotherapy. Examples of these promising technologies will be presented and their potential role in improving outcome modeling will be discussed.
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DOI：
10.1016/j.ijrobp.2021.01.042
发表时间：
2021-07-01
期刊：
International journal of radiation oncology, biology, physics
影响因子：
--
作者：
Cui S;Ten Haken RK;El Naqa I
通讯作者：
El Naqa I
影响因子：
82.9
作者：
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通讯作者：
Li, Quanzheng
影响因子：
3.8
作者：
Avanzo M;Wei L;Stancanello J;Vallières M;Rao A;Morin O;Mattonen SA;El Naqa I
通讯作者：
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影响因子：
5.2
作者：
Eckardt JN;Wendt K;Bornhäuser M;Middeke JM
通讯作者：
Middeke JM
肝细胞癌后使用成像和生物标志物对正常组织并发症进行建模。
DOI：
10.1016/j.ijrobp.2017.10.005
发表时间：
2018-02-01
期刊：
International journal of radiation oncology, biology, physics
影响因子：
--
作者：
El Naqa I;Johansson A;Owen D;Cuneo K;Cao Y;Matuszak M;Bazzi L;Lawrence TS;Ten Haken RK
通讯作者：
Ten Haken RK